Correlation vs. Causation and Bivariate Regression

This set of flashcards covers key terminology and concepts related to correlation, causation, and bivariate regression from the lecture notes.

Correlation

A relationship between two variables where they move in sync or happen at the same time, but does not imply one causes the other.

Causation Trap

The mistaken belief that just because two events occur together, one must be causing the other.

Spuriousness

A misleading relationship where a third hidden factor influences both observed variables.

Randomized Experiment

The gold standard of research that helps prove causation by randomly assigning subjects to experimental and control groups.

Intercept (b₀)

The predicted value of the dependent variable when the independent variable is zero, serving as a baseline for predictions.

Slope (b₁)

Indicates how much the dependent variable is expected to change with each one-unit increase in the independent variable.

R-Squared (R²)

Measures the proportion of variation in the dependent variable that is explained by the independent variable.

P-Value

A statistical measure that helps determine the significance of results, indicating the probability that the observed relationship is due to chance.

Ordinary Least Squares (OLS)

A method used in regression analysis to find the best-fit line by minimizing the sum of squared errors.

Total Sum of Squares (TSS)

The total variation of the data points around their average value.

Explained Sum of Squares (ESS)

The portion of total variation explained by the regression model.

Residual Sum of Squares (SSE)

The part of total variation that remains unexplained by the regression model.

Statistical Significance

A determination that a relationship observed in data is unlikely to be due to chance, often assessed using the p-value.